Experiments without either development or training data In the first setting, we aligned the input passive case frame to one of the active case frames of the same predicate only by using sim SEM and sim DIST with the parameter α = 1.


The other types of knowledge are automatically acquired from a large raw corpus.

although this paper focused on the activepassive alternation in Japanese, our framework is applicable to the other types of case alternation and to other languages, especially similar languages such as Korean.


Furthermore, the user-generated encyclopedia category tags are important clues if the entity exists in a encyclopedia.


For example, the head word of "•2è (Emperor Penguin)" indicates that it's a kind of "è (penguins)".

we put head words into the hypernym candidates.


In Chinese, head words are often laid after their modifiers.


Similarly "û" hints " nJ (lymphoma)" is a kind of diseases.

we use radicals as a feature the value of which is computed by using Equation 3. radical(e, h) = count RM (e, h) len(h) Here radical(e, h) denotes the ratio of characters radical-matched with the last character of the entity e in the hypernym h. count RM (e, h) denotes the count of the radical-matched characters in h. len(h) denotes the total count of the characters in h. Now training data is imperative to learn the weights of the features in Section 3.2.1.


When the candidates from all sources are merged, the coverage rate is further improved.

we set N as 10 in the remaining experiments.


Not all pairs from QUOTES raw are creative, and likewise, not all pairs from GLOSSES raw are uncreative.

we perform manual annotations to a subset of the collected pairs as follows.


These formulae mimic the ones discussed previously, but they are built under a different assumption: that the saliency (Giora, 1997) of a word's prior polarity might be more related to its posterior polarities score, rather than to sense frequencies.

we ordered posScore and negScore by strength, giving more relevance to 'valenced' senses.


Specifically, the first query contained only the first word from the babble, the second query only the first two words, and so on.

the number of queries presented to Indri for a given babble was equivalent to the number of words in the babble, with each query differing only by the number of words it contained.


To explore these lines, we need a map to reorganize all the information.

a "2-dimensional" story map is in bad need.


Inspired by the idea of classic "update summarization" task, we try to avoid those chronologically ordered documents sets focusing on a constant topic.

given the particularity of our task, we also have to develop the weighting function Γ with the temporal factor before starting the ranking algorithm.


Our fundamental assumption is that the interdate and inter-modal "links" have different influence comparing with the intra ones.

the core formula calculating the authority of c i is adapted as follows to make it become weight-compatible: C ′ is the subset of C which only includes the candidates of the same modality with c i , and C ′′ is the cross-modal candidates subset.


It can be proved that finding the optimal set L is an NP-Complete problem (not presented due to the limited space).

the brute-force exhaustive approach is crashed.


The essential idea of summarization is to reduce the data size, so that a more concise representation will be generated and help users to fast grasp the main points.

the Compactness of a story map needs to be guaranteed.


Obviously, only the verification of compactness is far from enough.

as mentioned in Section 3.2, the storyline set L we extract should keep alignment with the source collection, and contain informative as well as comprehensive information in C. we need to verify another property of our summarization, the Coverage.


These suggested that the more sparse the base features were, the more effective the corresponding meta features were.

we built the final parsers by adding the meta features of N1WM, N2WM, N3WM, and N4WM.


Only 327,864 (or 1.2%) features were added into OURS.

we used all the three types of meta features in our final meta parsers.


To address this issue, we introduce the L 1 norm from lasso technique (Tibshirani, 1996) to regularize the above likelihood function.

instead of maximizing the likelihood, we can minimize the loss function of the negative log-likelihood with a linear penalty: where λ 1 is the regularization coefficient.


The reason is because adding the L 1 norm will make the objective function not continuously differentiable, where as the L 1 constraint could be just a simple linear constraint (Lee et al., 2006).

the alternative L 1 -constrained problem could be defined as: To test the robustness of Laplacian structured sparsity term in the setup of a L 1 -constrained problem, we can incorporate the Laplacian penalty term into the above formula, and derive: Note that the Laplacian matrix is positivesemidefinite, because this graph Laplacian penalty can be viewed as a quadratic term, and the objective function in equation 19 is now convex differentiable and will produce sparse estimates, so that we are able to use a limited-memory projected quasi-Newton method (Schmidt et al., 2009) to solve the dual form of this problem.


However, one-step associativity may not fully discover the underlying relatedness between products due to the fact that the product associativity is indeed transitive.

a transitable associativity model is needed.


A good weighting method should be able to leverage commercial interests/intents of users well and emphasize the keywords users really focus on.

we consider making use of the retweeting (a.k.a.


It indicates one-step similarity method is not able to fully capture the real associativity between products.

we propose to use the random walk method to propagate the relatedness score on the product graph.


end of the corresponding syntactic constituents.

the terminal symbols of the English tree are sorted in a Japanese-like order.


Embeddings of direct translations overlap, and semantic relationships across bilingual embeddings were further improved through unsupervised learning on a large unlabeled corpus.

we produce for the research community a first set of Mandarin Chinese word embeddings with 100,000 words trained on the Chinese Gigaword corpus.


character sequences) as basic units for labeling, whereas we consider single characters.

labeling is more resource-intensive, but it also gives us more expressive power.


have been singled out by developmental psychologists as part of "core knowledge" -a set of early-learned or perhaps innate concepts upon which the rest of cognition is built (Spelke and Kinzler, 2007).

these semantic features/predicates may be not only crucial to describing linguistic meaning but may be central organizing principles for a human's (reasonably successful) thinking about and conceptualization of the world.


Rare is the non-linguist who can immediately answer the question, "Does the verb 'throw,' when used transitively, entail a change of location on the part of its THEME?"

we began by developing tasks that isolate semantic features in a way accessible to untrained annotators.


Thus, these appear to be cases of disagreement, either because the correct intuition requires more work to obtain or because of differences across idiolects (at the moment, there is no obvious pattern as to which sentences caused difficulty, but the sample size is small).

follow-up investigation of sentences with little inter-coder agreement may be warranted.


Clearly, the mere use of a second-order method improves results, regardless of the base measure.

far we have considered authorial corpora consisting of only ten documents.


It turns out that given all the parsing features, the margin is the most discriminative meta-feature.

when it is present, other metafeatures we added do not help much, we do not include them in our experiments due to overhead.


From Figure 3, however, we notice that even with the first feature group which only contains one feature template, the non-projective parser can almost achieve 50% accuracy.

ideally, our policy should have locked that many edges after the first iteration.


Following the work (Prettenhofer and Stein, 2010), we used the original English reviews as the source language while treating the other three languages as target languages.

we construct nine cross language sentiment classification tasks (GB, GD, GM, FB, FD, FM, JB, JD, JM), one for each target language-category pair.


When collecting counts for the syntactic and informativeness scores, we used 9M news articles crawled from the Internet, much more than Filippova & Strube (2008).

our estimates are probably more accurate than theirs.


Moreover, we observed poor parsing accuracy for headlines which would make syntactic annotations for headlines hardly useful.

instead of taking the headline as it is, we use it to find a proper extractive compression of the sen-tence by matching lemmas of content words (nouns, verbs, adjectives, adverbs) and coreference IDs of entities from the headline with those of the sentence.


For exam-ple, the ICSI system (Gillick et al., 2008) removed the sentences that are too short or have non-overlap with the queries, and concepts with document frequency less than 3, resulting in 95.8 sentences and about 80 concepts per topic on the TAC2009 dataset.

the actual scale of ILP is rather small after pruning (e.g., 176 variables and 372 constraints per topic).


These methods successfully improved summarization quality, but they did not consider the rhetorical relations between the textual units of a source document.

summaries generated by these methods may lack logical coherence.


Since these methods are based on subset selection, the summaries they generate cannot preserve the rhetorical structure of the textual units of a source document.

the resulting summary may lack coherence and may not include significant textual units from a source document.


Unfortunately, the RST-DT does not allow it, because textual units in the RST-DT are located only on leaf nodes and parent-child relationship between textual units are represented implicitly at higher positions in a tree.

we first propose rules that transform an RST-DT into a dependency-based discourse tree (DEP-DT) that explicitly defines the parent-child relationships.


The DEP-DT expresses the parent-child relationship between the EDUs.

we have to drop e 7 , e 9 and e 10 when we drop e 8 .


However, both the browsing and the searching give the user a list of relevant contents (e.g., questions shown in Table 1), not the direct knowledge.

the user has to read these contents, understand them, classify them into various topics, and gain valuable The above problem calls for a new approach to structuralize UGC in social media, which facilitates users to seek knowledge (e.g., travel information about Edinburgh) more effectively.


For social tagging, it is not applicable in many sites and has sparsity problem (Shepitsen et al., 2008).

both topic models and social tagging are not suitable for structuralizing UGC in social media.


In addition, they usually give descriptions of entities, which prevent the interpretation problem.

cET is more suitable for structuralizing UGc.


In an ET, some entities are more similar than other entities which share the same parents.

on each layer of the ET we cluster entities with the same parents (e.g., "london", "nidry street south", and "city center" on layer 2 of Fig.


Under the same parent, some entities 5 may share similar topics.

the final step is to hierarchically cluster entities with the same parents at different layers of entity trees.


For instance, if an entity is extracted from questions in the category Computers & Internet, and it appears two or more times in Computer and Internet categories in Freebase, it will be filtered.

each entity is attached with a unique Freebase category label (i.e., the ground truth for clustering).


From Table 5 we can find that, although AC-MAX's accuracy varies across categories (e.g., the F 1 of Transportation is much higher than that of Travel), it performs well in general.

we adopt AC-MAX with θ = 0.1 for hierarchical entity clustering.


Each volunteer is asked to finish 12 tasks (6 knowledge-learning tasks and 6 question-search tasks) using the CET-based program and 12 other tasks using the list-based program in random order.

each task is finished by exactly 8 different participants using each program.


Two nearly identical posts belong in different categories if one mentions a patient and the other does not.

our aim is to create features that identify references to a specific patient and use these to more accurately distinguish the two types of posts.


Our experiments also confirm that the super target lexical chains with multiple translation options for each chain word are better than the target lexical chains with only one translation per chain word.

we build our cohesion models based on the super target lexical chains, which will be described in the next section.


we have where min{x, y} returns the minimum of the two values x and y.

we could actually eliminate the q variables and write an optimization problem that is identical to the IBM Model 2 optimization problem, but with the objective function It will turn out that both views of the I2CR-1 problem-with and without the q variables-are helpful, so we have included both in this paper.


These finite state acceptors are then converted into transducers by modifying the arcs: split the labels of each arc, x:y, making x the input label for that arc, and y the output label.

traversing such an arc will consume an x a return a y.


Combining these factors, we see that, for spellings, more edit operations are required, and there are more symbols to which to apply them.

for spellings, there are more incorrect candidates.


This argument also applies to finding alternative pronunciations.

the roundtrip method offers more possible mappings.


One possible explanation is that the variety of orthographic alternations is greater than that of pronunciation alternations.

the training set for spelling may provide less relative coverage of the alternations in its test set than the training set for pronunciation provides for its test set.


Furthermore, according to the copyobject model the meaning of a verb phrase computed by a verb matrix ij v ij ( − → n i ⊗ − → n j ) and an object vector j o j − → n j becomes: Finally, the Kronecker model has no meaning for verb phrases, since the vector of a verb phrase will become ( −  meaning of a verb phrase becomes a scalar multiplication of the meaning of its verb.

the cosine distance (used for measuring similarity) between the meanings of two verb phrases is reduced to the distance between the vectors of their verbs, completely dismissing the role of their objects.


This comes from the observation that the inner product of every two column vectors in X is the inner product of the corresponding column vectors of ΣV T , which can be derived from the equations below.

the semantic relatedness between the i-th and j-th words can be computed by cosine similarity 1 : When used to compare words, one well-known limitation of LSA is that the score captures the general notion of semantic similarity, and is unable to distinguish fine-grained word relations, such as antonyms (Landauer and Laham, 1998;Landauer, 2002).


In MRLSA, m (the number of relations) is usually small, while d and n are typically large (often in the scale of hundreds of thousands).

we choose R 1 = R 2 = τ , τ d, n and R 3 = m, where τ is typically less than 1000.


However, MRLSA generalizes LSA to model multiple relations, which could be obtained from both homogeneous and heterogeneous data sources.

the performance of a target task can be further improved.


Therefore, it is interesting to check if combining synonyms and antonyms from Encarta helps.

mRLSA:WordNet+Encarta improves over mRLSA:4-layers significantly.


A low-rank approximation of the tensor is then derived using a tensor decomposition.

words in the vocabulary are represented by vectors in the latent semantic space, and each relation is captured by a latent square matrix.


When operating with 2 languages, the context features cannot be compared directly.

in order to compare the feature vectors cv(w S 1 ) and cv(w T 2 ), the context features need to span a shared bilingual vector space.


However, there seems to be an apparent flaw in logic, since the methods assume that there exist readily available bilingual lexicons that are then used to induce bilingual lexicons!

the focus of the researchers has turned to designing BLE methods that do not rely on any external translation resources such as machine-readable bilingual lexicons and parallel corpora (Haghighi et al., 2008;Vulić et al., 2011).


Only the brand names and product names are different.

using individual reviews for modeling is not very effective.


The Spanish bigram "oriente medio" poses the same problem.

without considering word reordering, the model described by Dou and Knight (2012) is not a good fit for deciphering Spanish into English.


Using α p , α s α σ 1 tended to recover useful segmentations, but we have not yet been able to find reliable generic priors for these values.

we selected them empirically to obtain a stem vocabulary size on the parallel data that is one-to-one with English.


To capture target context effects, we depend on strong target language models.

an important extension of our work is to explore the interaction of our approach with more sophisticated language models that more directly model morphology, e.g., the models of Bilmes and Kirchhoff (2003), or, alternatively, ways to incorporate target language context in the inflection model.


Probabilistic structured queries that include context-aware estimates of translation probabilities require a preservation of sentence-wise contextsensitivity also in retrieval.

unlike the direct translation approach, we compute weighted term and document frequencies for each sentence s in query F separately.


We observed that the performance of the Boost method continuously improved with the number of iterations T and with the number of samples S, but saturated at about 15-20 samples without visible over-fitting in the tested range of T .

we arbitrarily stopped training after obtaining 5, 000 features per sample, and used 35 samples for uni-gram version and 65 samples for the combined bi-gram version, resulting in models with 104K and 172K unique features, respectively.


The event-event relation set R contains the following (assuming a labeled edge (u, v, r)): 1.

pREV denotes that u is an event immediately before v. the edges (u, v, pREV) and (v, w, pREV), preclude the edge (u, w, pREV).


However, an undirected graph P is connected iff there exists a directed tree that is a subgraph of P when edge directions are ignored.

the resulting formulation is almost identical and is based on flow constraints which ensure that there is a path from a designated root in the graph to all other nodes.


The chain structure is one of the several soft constraints we enforce.

our modified objective function is ijr θ ijr y ijr + k∈K α k C k , where K is the set of soft constraints, α k is the penalty (or reward for desirable structures), and C k indicates whether a constraint is violated (or satisfied).


However, since the structure is deterministically fixed, Chain is highly inflexible and does not allow any extensions or incorporation of other structural constraints or domain knowledge.

it can be used as a simple and efficient approximation but is not a good candidate for a real system.


, and a part of the template strand is either skipped by the replication machinery or used twice as a template.

a segment of DNA is deleted or duplicated."


In this paper, we present a novel approach that combines latent and explicit topic modelling approaches in the sense that it builds on a set of explicitly defined topics, but then computes latent relations between these.

the method combines the benefits of both explicit and latent topic modelling approaches.


Typical choices for this association measure φ are the sum of the TF-IDF scores or an information retrieval relevance scoring function such as BM-25 (Sorg and Cimiano, 2010).

for the case of Tf-IDf, the value of the j-th element of the topic vector is given by: the mapping function can be represented as the product of a Tf-IDf vector of document d multiplied by an W ×N matrix, X, each element of which contains the Tf-IDf value of word i in document b j : for simplicity, we shall assume from this point on that all vectors are already converted to a Tf-IDf or similar numeric vector form.


In this context, we consider two forms of normalization, term and document normalization, which can also be considered as row/column normalizations of X.

a straightforward approach to normalization is to normalize each column of X to obtain a matrix as follows: If we calculate X T X = Y then we get that the (i, j)-th element of Y is: 4 In the experiments in the next section we discarded 4.2% of documents at N1 = 1000 and 47% of documents at N1 = 5000 the diagonal of Y consists of ones only and due to the Cauchy-Schwarz inequality we have that |y ij | ≤ 1, with the result that the matrix Y is already close to I.


With the help of an AES system that highly agrees with human raters, the human workload can be reduced by half at most.

the agreement between the AES system and the human rater is an important indicator of an AES system's effectiveness.


If an AES system frequently disagrees with the first human rater, a second human rater will be needed in most cases.

the introduction of the AES system does not bring much benefit in reducing the human workload.


Pairwise approaches process a pair of documents each time and usually model ranking as a pairwise classification problem.

the loss function is always a classification loss.


Production rules are overly specific to gain more generalized, interpretable, high-level insights however (Feng et al., 2012).

similarly as word categories (POS), we consider the categories of nonterminal nodes of the parse trees, in particular, phrasal and clausal tags, as they represent the gist of constituent structure that goes beyond shallow syntactic information represented by POS.


Of course, considering only the distribution of phrasal tags is significantly less informed than considering numerous other features that have shown substantially better performance, e.g., unigrams and CFG rewrite rules.

we also present the SVM classifier trained on unigram features.


All the event-mentions in the EEC-set may be semantically equivalent and are hence candidates for a good paraphrase cluster.

the paraphrasing problem becomes a prediction problem: for each relation r i in the EEC-set, does it or does it not describe the hypothesized event?


Our method rests on the simple observation that an entity which participates in many different events on one day is likely to have participated in events in recent days.

we can judge whether an entity pair is good for paraphrasing by looking at the history of the frequencies that the entity pair is mentioned in the news streams, which is the time series of that entity pair.


Since our baseline system has approximately 80% accuracy at this stage, it is reasonable to assume that at least one of the argument mentions is correctly disambiguated.

we prune the search space by making only two queries for each mention pair (m i , m j ): q 0 = (t * i , m j ) and q 1 = (m i , t * j ) where t * i , t * j are the strings representing the top titles chosen by the current model for mentions m i , m j re-spectively.


So, the l th constraint can be enforced by subtracting (ρ 2 • w l 2 ) from the objective function given by Equation (1).

a penalty of ρ 2 would be enforced iff this constraint is violated.


Equivalently, the k th constraint can be written as follows in boolean algebra notation: For the k th constraint, we introduce one more variable z k ∈ {0, 1} which satisfies the following condition: Using boolean algebra, it is easy to show that Equation (5) can be reduced to a set of linear inequalities.

we can incorporate the k th con- straint in the optimization problem by adding to it the constraint given by Equation 5and by subtracting (ρ 3 (1 − z k )) from the objective function given by Equation (1).


It should also be noted that BC performed significantly better than B for all the categories.

the constraints are helpful even in the absence of knowledge-based features.


Burger's corpus does not include Russian and Spanish data on the same scale as English.

for Russian and Spanish we construct a new Twitter corpus by downloading tweets from followers of region-specific news and media Twitter feeds.


an automated Opinion Mining solution is very attractive.


Also, different parts of one article can contain different opinions (Watson and Noble, 2007).

we work with extracted statements of news articles, in which a sequence of consecutive sentences has the same tonality value.


Subsequently, an average subjective measure vector selects the most subjective terms.

they require hardly any natural language preprocessing except a sentence splitter and a tokenizer.


Furthermore, we also include negation particles.

the vocabulary V is the set of words in lemma for one set of statements S. Thus, for every lemma w ∈ V , the approach creates one node υ in the graph.


Our approach recognizes the nodes for "be", "structural", "factors", "growth", and "story".

the subgraph G sl for the first sentence (l = 0) would be the graph which is drawn in solid lines in figure 2.


The result of this method is a classification of words and phrases.

for a statement classification, we classify the words of the statements and the class of the most frequently used words is the class of the statement (ambiguous statements are classified as the most frequent class).


SO-CAL (Taboada et al., 2011) needs dictionaries with sentiment values from -5 to +5 with intervals of one.

we use the same scores as the Wilson method and a word with a value above 0.818 to 1 gets a sentiment score of +5 and so on.


The accuracy of the dictionary variant is 43.44% on PDS and 40.12% on Finance.

the tonality classification by the most frequent word class seems appropriate for this task and method, because this method achieves better results in the classification of statements than on the word level.


We simply assume that each opinionated sentence has one opinion target, which is consistent with Candidate Similarity: Prior labeling: for vV Filtering Matrix: for vV Probability: p inj and p cont Output: Label vector: for all vV do : end for 9: until convergence the statistical result of our dataset that over 93% sentences have only one opinion target and each sentence has an average of 1.09 targets.

the most confident candidate of each sentence will be selected as the opinion target.


APCluster+HS means that all the hashtags are retained as normal texts for calculating message similarity.

the clustering performance can be largely improved.


The above results reveal that our proposed unsupervised label propagation algorithm works well in pseudo topics and the performance can be increased with better clustering results.

we can try to incorporate other social network information to improve the message clustering performance, which will be studied in our future work.


When it is not an entity, it takes one of the other four values: "general position" (i), "topic-specific position" (o), "topic" (t), or "background" (b).

every term w is drawn from one of these 5 types of bags, and y acts as a switching variable to select the type of bag.


However, it groups event tweets into a fixed number of bursty topics and then uses a twostate machine in a postprocessing step to identify events from these bursty topics.

events are not directly modeled within the generative process itself.


First, tweets are posted as single units and do not necessarily refer to each other.


As a result, texts written by authors that tend to use k-signatures are likely to be easily identified by a reasonable classification algorithm.

k-signatures provide a possible explanation for the high quality results presented in this paper.


Moreover, in real life applications we are often not even sure that the real author is part of our training set.

we allow our system to respond "don't know" in cases of low confidence (Koppel et al., 2006;Koppel et al., 2011b).


The best split point and score cannot be computed until all scores are available.

there is a separate Viterbi step after chart scoring.


The gap between GPU and CPU performance is large enough that CPU Viterbi search was a bottleneck, even though it requires asymptotically less work (O(Gs 2 ) worst case, O(Gs) typical) vs O(Gs 3 ) to compute the CKY scores.

we wrote a non-recusive GPU-based Viterbi search.


By annotating syntactic categories with their distributional representation, the method emulates lexicalized approaches (Collins, 2003) and captures similarity more flexibly than solutions based on hard clustering (Klein and Manning, 2003;Petrov et al., 2006).

their approach mainly aims at improving parsing by capturing a richer, data-driven categorial structure.


The rationale is that the correct (wrong) bracketing will lead to semantically more (less) plausible phrases.

a measure able to discriminate semantically plausible from implausible phrases should also indicate the most likely parse.


This non-lexical feature representation makes different target word/substitution pairs in different contexts 4 directly comparable.

it becomes possible to learn an all-words system that is applicable to unseen words, using supervised methods, which provides superior ranking accuracy to unsupervised and knowledge based models.


Rather, as our results show, less complex models tend to underfit the data.

we believe that more complex models can achieve a better performance, of course with an increased computational cost.


The authors have designed a dynamic process following the principle that every update (u, i) needs to be instantly taken into account by the system.

we have to update the σ 2 w and IDF(w) at each iteration, for every word.


It should be noted that we are not able to make online experiments.

we can not measure the feedback on our recommendations.


Indeed, for a given segment, the value in Equation 5 is constant everywhere except where we intersect any of the coordinate hyperplanes, i.e., where one of the coordinates is zero.

our method consists of evaluating Equation 5 at the intersection points between the line and coordinate hyperplanes, returning the optimal point within the given segment.


A constant number of arithmetic operations suffice to compute the final expected loss value and its gradient.

computing the gradient does not increase the algorithmic complexity when compared to conventional approaches using coordinate ascent and random directions.


Of course, it is difficult to compare overall runtime of convex optimization with that of MERT, as we know of no way to bound the number of gradient evaluations required for convergence with MERT.

we resort to empirical comparison later in the paper, and find that the two methods appear to have comparable runtime.


However, since PRO optimizes translation length on the Dev dataset and MERT does so using the Tune set, a comparison of the two systems would yield a discrepancy in length that would be undesirable.

we add another hyperparameter to regularized MERT to tune length in the same manner using the Dev set.


These results echo the category-level findings, that word-level brain activity is also organised in a similar way across lobes.

this diminishes our chances of uncovering neat interactions between models and brain areas (where for instance the Window2 model correlates with the frontal lobe and Object model matches the occipital lobe).


In order to be tractable here too, we ensure that φ also factorize according to the same structure as f .


For example, the phrase "podívali jsme se" translates to "we looked", and the word jsme would typically be aligned to we; se, which serves as a reflexive pronoun here, remains unaligned.

in our data, over 7000 occurrences of se appear, but only 17 instances have a tag projection that is not filtered by Wiktionary.


Section 4), our task is to model interestingness between unstructured documents, i.e., without access to any document structure or Web interaction data.

in our experiments, We collect a large dataset of user browsing events from a commercial Web browser.


We restrict our browsing events to Wikipedia since its pages tend to contain many anchors (79 on average, where on average 42 have a unique target URL).

they attract enough traffic for us to obtain robust browsing transition data 2 .


In our experiments, we used different settings for the two interestingness tasks.

we postpone the detailed description of these datasets and other task-specific datasets to Sections 4 and 5.


The translation-based approach allows any pair of non-identical but semantically related words to have a nonzero matching score.


This is confirmed by results presented in (Le et al., 2012), which show that a word-factored translation model outperforms the phrase-factored version.

in this work we continue relying on source and target word vocabularies for building our phrase representations.


Only when the presentation of the source side is finished we start estimating probabilities for the target side.

we do not let the neural network learn a target distribution until the very last source word is considered.


In the tables, we include those phrase-based variants which perform best on the dev data, where phrase-based JMs always are at least as good or better than the corresponding TMs in terms of BLEU.

we mainly report JM results for the phrase-based networks.


Models for selectional preference need to adequately deal with the consequences of Zipf's law: language is inherently sparse, and the majority of language utterances occur very infrequently.

models that are based on corpus data need to properly generalize beyond the mere co-occurrence frequencies of sparse corpus data, taking into account the semantic similarity of both predicates and arguments.


For instance, in the corpus compiled by Stab and Gurevych (2014) only 37% of the premises appear adjacent to a claim.

existing approaches of discourse analysis, e.g.


They argue that existing definitions of discourse relations are only relevant as a building block for identifying argumentative discourse and that existing approaches do not contain a single relation that corresponds to a distinct argumentative relation.

they consider a set of 12 discourse relations from the RST Discourse Treebank (Carlson et al., 2001) as a single argumentative relation in order to identify justifications for a given claim.


Thus, if the type of the argument component is reliably identified many potential pairs can be excluded.

we define two features that represent the argumentative type of the source and target components identified in the first experiment.


Nevertheless, we obtain almost human performance when including the types of argument components of the gold standard (macro F1-score >0.85) in our argument relation identification experiment and when including the number of incoming and outgoing support relations for each argument component in our first experiment (macro F1-score >0.9).

it can be assumed, that if the identification of argument components can be improved, the identification of argumentative relations will achieve better results and vice versa.


Due to the confirmation and clarification mechanisms defined above, the central controller becomes a sequential decision maker that must take the overall smoothness of the dialogue into account.

we propose an MDP-based approach for learning an optimal central control policy in this section.


If online reinforcement learning is used, we will be unable to evaluate the generated policy after each update, and hence will not know which policy to keep for the final evaluation.

we do a batch policy update and iterate the learning process for a number of batches, such that the data collection phase in a new iteration yields an evaluation of the policy obtained from the previous iteration at the same time.


The same finding is indicated in Figure 3 as well, when we use the domain selection accuracy as the evaluation metric.

we kept the policy corresponding to the peak point of the learning curve for the comparison experiments below.


For an individual labeling Z l c,p , we measure the quality based on the confidence scores, f l,c,p , returned by the argument classifier.

the objective function can be defined as (2) As the key to the success of ILP-based joint inference, the following constraints are employed: Constraint 1: The arguments corresponding to a connective cannot overlap with the connective.


Evaluating against characters with all four labels do not really represent the models' difference as it is deemed to be consistent.

beyond evaluating against all characters in the Wikipedia test set (referred to as Full measurement), we also evaluate against characters that are only constrained with less than four labels (referred to as Label measurement).


With their method, informal spellings are merely recognized and not normalized.

they did not investigate how to exploit the information obtainable from well-spelled equivalents to increase word segmentation accuracy.


Since lexical normalization is a relatively new research topic, there are no precise definitions of a lexical normalization task that are widely accepted by researchers.

it is important to clarify our task setting before discussing our joint model.


Similar words are expected to be distributed close to one another in the embedding space.

word embeddings can be beneficial for a variety of NLP applications in different ways, among which the most simple and general way is to be fed as features to enhance existing supervised NLP systems.


Moreover, each prototype word is also its own prototype (since a word has maximum similarity to itself).

if the prototype is closely related to a label, all the words that are distributionally  Various tasks can be considered to compare and analyze the effectiveness of the above three approaches.


We also consider different number of clusters n, to take advantage of the different granularities.

the dimension of the cluster features is much higher than that of BinarizedEmb and DistPrototype.


Although the dense embedding features are lowdimensional, the feature vector for each word is much denser than in the sparse and discrete feature space.

we actually need much more computation during decoding.


Moreover, disfluencies in speech also introduce noise in downstream tasks like machine translation and information extraction (Wang et al., 2010).

punctuation prediction (PU) and disfluency prediction (DF) are two important post-processing tasks for automatic speech recognition because they improve not only the readability of ASR output, but also the performance of downstream Natural Language Processing (NLP) tasks.


On the other hand, for the second utterance, if disfluency prediction is performed first, it might mark "I am sorry" as disfluent in the first place and remove it before passing into the second-stage punctuation prediction.

no matter which task is performed first, certain utterances can always cause confusion.


For hard cascade, the input sequence is modified prior to feature extraction.

many of the features generated by the feature templates given in Table 3 will be affected by these modifications.


It may be possible that the information contained in the two models for the soft-cascade PU→DF mostly overlaps with the information contained in the other two models for the soft-cascade DF→PU since all the four models are trained using the same features.

no additional information is gained by combining the four models.


In fact, Algorithm 1 of (Biçici and Yuret, 2011) is precisely the accelerated greedy algorithm of (Minoux, 1978) applied to the submodular function corresponding to f (X) = ∑ u∈U f u (X), and Algorithm 1 of (Biçici, 2013) is the cost-normalized variant of this greedy algorithm corresponding to a knapsack constraint (Sviridenko, 2004).

our analysis shows that these methods also have a 1 − 1/e performance guarantee and also the O(n log n) empirical complexity mentioned in Section 2.


It is important to note that we are using submodularity as a "model" of the selection process, and the submodular objective acts as a surrogate for the actual SMT objective function.

the mathematical guarantee we have is in terms of the surrogate objective rather than the true SMT objective.


In general, longer n-grams might be considered more valuable since they typically lead to better translations and are more relevant for BLEU.

we include a reward term for longer n-grams in the form of β |u| where β ≥ 1 and |u| is the length of feature u.


Since target phrases are usually in very high dimensional space, traditional linear classification model is not suitable for our problem.

we treat our problem as a ranking problem that can handle large number of classes and optimize the objectives with scalable optimizer stochastic gradient descent.


Contextual features extracted from source sentence are strong enough to discriminate different Source Sentence 4 Nearest Neighbor from bilingual embedding 只有稳定的社会环境， 投 资 者 才 才 才 能 能 能 踏 踏 实 实 地 做 生 意 。(Investors can only get down to business in a stable social environment) will be, can only, will, can 在比赛与交往中，中国 残 疾 人 显 示 了 非 凡 的 体育才 才 才能 能 能。(In competitions, the Chinese Disabled have shown extraordinary athletic abilities) skills, ability, abilities, talent 在哥国的自然环境下， 葡 萄 是 无 法 正 常 开 花 结 结 结 果 果 果 的 。(In the natural environment of Costa Rica, grapes do not normally yield fruit.)

fruit, outcome of, the outcome, result 结 结 结 果 果 果 ， 东 区 区 议 会 通 过 一 项 议 案 。(Eastern District Council passed a proposal) in the end, eventually, as a result, results word senses.


For further understanding, we first describe some definitions.

given a word-aligned sentence pair (F I 1 , E J 1 , A), an alignment set A set (i) is the set of target word positions that aligned to the source word F i i : For example, in Figure 1, the alignment set for the Chinese word "shaoshu 3 " is the alignment span for the Chinese word "shaoshu 3 " is A span (3) = [5, 10].


defined a symmetric feature for each target/source position pair, i, j as follows: The feature assigns 1 for the subset of word alignment for − → y , but assigns −1 for ← − y .

if a word pair i, j is aligned with equal posterior probabilities in two directions, the expectation of the feature value will be zero.


The f2f constraint function is defined as follows: This constraint function returns a non-zero value for a word pair (i, j) when they are function words.

the pair of function words are encouraged to agree with each other, but not other pairs.


However, although it is standard practice in MT evaluation to measure increases in automatic metric scores with significance tests (Germann, 2003;Och, 2003;Kumar and Byrne, 2004;Koehn, 2004;Riezler and Maxwell, 2005;Graham et al., 2014), this has not been the case in papers proposing new metrics.

it is possible that some reported improvements in correlation with human judgment are attributable to chance rather than a systematic improvement.


In the bilingual setting, inputs to the algorithm are translation options, which can be overlapping and mutually exclusive, and not necessarily all of which are included in the output.

the decoder needs to perform word selection in addition to word ordering.


This means that the probabilities can be calculated before translation.

the predicted probabilities can be integrated into the decoder conveniently as soft constraints and no extra time is added during decoding.


In addition, it is very difficult or even impossible to collect an extra large corpus for some special domains such as the TED corpus (Cettolo et al., 2012) or for some rare languages.

to improve the performance of LMs, without assistance of extra corpus, is one of important research topics in SMT.


Operators of length four like 2, 4, 1, 3 (Wu, 1997) are presumably more complex than 2, 1 , whereas operators longer than four signify even more complex order difference.

we devise possible branching feature functions over the operator length for the nodes in PETs: • factor 2 -with two features: ∆ [ ] and ∆ <> (there are no nodes with factor 3 (Wu, 1997)) • factor 4 -feature ∆ =4 • factor bigger than 4 -feature ∆ >4 Consider permutations 2, 1, 4, 3 and 4, 3, 2, 1 , none of which has exactly matching ngrams beyond unigrams.


Kendall's tau τ is calculated as follows: where a concordant pair indicates two translations of the same sentence for which the ranks obtained from the manual ranking task and from the corresponding metric scores agree (they disagree in a discordant pair  are concordant).

an automatic evaluation metric with a higher τ value is making predictions that are more similar to the human judgments than an automatic evaluation metric with a lower τ .


Our goal is to develop a classifier of such tuples that decides whether t 1 is a better translation than t 2 given the reference r. Engineering features for deciding whether t 1 is a better translation than t 2 is a difficult task.

we rely on the automatic feature extraction enabled by the SKL framework, and our task is reduced to choosing: (i) a meaningful structural representation for t 1 , t 2 , r , and (ii) a feature function φ mt that maps such structures to substructures, i.e., our feature space.


However, this simple mapping may be ineffective since the trees within a pair, e.g., (t 1 , r), are treated independently, and no meaningful features connecting t 1 and r can be derived from their tree fragments.

we model φ M (r, t 1 ) by using word-matching relations between t 1 and r, such that connections between words and constituents of the two trees are established using position-independent word matching.


For example, in Figure 1, the thin dashed arrows show the links connecting the matching words between t 1 and r. The propagation of these relations works from the bottom up.

if all children in a constituent have a link, their parent is also linked.


The hidden layer captures the 5 the fact that we can classify accurately during windows 300-400ms indicates that the classifier is taking advantage of the N400 response discussed in the introduction context before word i is seen.

it seems reasonable that the hidden layer is not only related to the activity when the word is on the screen, but also related to the activity before the word is presented, which is the time when the brain is integrating the previous words to build that context.


Note that the learned similarity scores reflect the semantic distance among words whereas the WordNet scores reflect semantic closeness.

a negative correlation is best in our evaluation, where the value of -1 corresponds to the maximum correlation.


Note that Steyvers and Tenenbaum (2005) made the empirical observation that small-world networks of semantic knowledge had a single connected component that contained the majority of nodes in the network.

in addition to σ g , we also measure the relative size of a network's largest connected component having size N lcc : Scale-free structure.


The pair tokens {(w n , w n+j ) : −t(n) ≤ j ≤ t(n), w i ∈ S} are then appended to the training data.

target/context training pairs are such that (i) only words within a k-window of the target are selected as context words for that target, and (ii) words closer to the target are more likely to be selected than those further away.


In future studies we hope to compare the present approach to architectures with domain-specific conceptual memories.

for each concept w in CSLB, we then construct a feature bag b(w) by appending lexical forms to b(w) such that the count of each feature word is equal to the strength of that feature for w. when features are sampled from b(w) to create pseudo-sentences (as detailed previously) the probability of a feature word occurring in a sentence reflects feature strength.


Therefore, we can make better corrections using such context information on nodes.

our system corrects grammatical errors on dependency trees directly.


The sub-sequences are not identical to the original text.

instead of using ordinary language models, we should train special language models using the sub-sequences to measure the correctness of a production rule.


Similar to the previous work (Xing et al., 2013), we find that adding more prepositions will not improve the performance in our experiments.

we only consider a fixed set of prepositions: {in, for, to, of, on}.


We find ME achieves the highest accuracy.

we use ME as the classification model in our system.


LP relaxation algorithms for the MAP problem define an upper bound on the original objective which takes the form of a linear program.

a minimum of this upper bound can be found using standard LP solvers or, more efficiently, using specialized message passing algorithms (Yanover et al., 2006).


First, it does not utilize supervision in the form of either a supervised syntactic parser and/or manually crafted SCF rules.

it induces unnamed frames (clusters) that are not directly comparable to the named frames induced by previous systems.


On the other hand, the proposed approach does not only make use of the general context information in a large corpus of social media text, but it also makes use of the context of the OOV word in the input text message.

an OOV word can be normalized to different IV words depending on the context of the input text.


Since some social media text messages are extremely short and contain several OOV words, they do not provide sufficient context, i.e., IV neighbors, to enable the extraction of good candidates from the word association graph.

we extended the candidate list obtained through contextual similarity as described in the previous section, by including all the tokens in the word association graph that satisfy the edit distance and 3 an approximate string comparison measure (between 0.0 and 1.0) using the edit distance https://sourceforge.net/projects/febrl/ phonetic edit distance criteria.


For the most part, the aforementioned knowledge bases are constructed using manual techniques and carefully supervised information extraction algorithms.

they obtain high coverage on head attributes, but low coverage on tail ones, such as those shown in Table 1.


known facts) for such attributes, especially in the long tail.

reNoun has an initial seed fact extraction step that automatically generates a small corpus of relatively precise seed facts for all attributes, so that distant supervision can be employed.


Note that in our example the phrase executive chairman, which is also in our ontology of attributes, is not part of any coreference cluster.

the fact centered around this attribute in the example will not be part of the seed extractions, but could be extracted in the next phase.


ENWIKI13 especially has dense coverage across the entire world, whereas TWUS only covers the United States and parts of Canada and Mexico.

there are a much larger number of non-empty cells at a given resolution and much coarser resolution required, especially with the uniform grid.


Similar examples with the pattern 'NP + / + VV' can be found in PropBank, in which the subject NP of the sentence is usually not an argument of the latter predicate.

'/' (is) is an effective clue to detect this kind of identification error.


It is challenging to obtain a fully correct syntax tree for a complex sentence with multiple predicates.

base features that heavily rely on syntax trees often fail in discriminating arguments from candidates as demonstrated in Figure  3.


Conjunctions are often used to concatenate two clauses, but the first clause commonly describes one proposition and the second clause describes another one.

if the candidate and the given predicate have different positions relative to the conjunctions, the candidate is usually not the argument of the given predicate.


We first note that in testing set, there are a large number of shared arguments, which weighs about one quarter of all arguments in Chinese and 14% in English.

the fine processing of these arguments is essential for argument classification.


In the corpus, a prepositional phrase is commonly labeled as ARGM-LOC and ARGM-TMP.

the base model labeled the argument into these classes but one as ARGM-LOC, another as ARGM-TMP.


From Table 1, we can see that the role transition between ARGM-TMP and ARGM-LOC is impossible, which lowers the score of candidates containing both ARGM-LOC and ARGM-TMP in the joint model.

the joint model is more likely to output the gold candidate.


Furthermore, the cardinality of the set of possible segmentations is combinatorially large.

we decompose Equation 1 using the factor graph depicted in Figure 3.


Pierrehumbert and Hirschberg (1990) claim that intonational phrases play an important role in discourse segmentation.

we hypothesize that the edges of intonational phrases are very likely to correspond with correct splitpoints.


Chafe (1988) confirms that punctuation in written language has a strong correspondence with intonational phrase boundaries, and an assumption like ours has been successfully implemented in speech synthesis systems (Black and Lenzo, 2000).

we include bigrams containing punctuation as splitpoints in our proxy labels.


The identification of the splitpoint "and" comes from the fact that this word has, among other uses, a discourse connective meaning.

the edges in our trellis between different events are weighted higher than edges between the same event in the edges between the nodes for "highest" and "and", encouraging the Viterbi path to change events at this point.


Our current setup cannot increase recall: we can only improve the precision of the utterance-level alignments we are given.

if an event is overlooked in the first-pass of utterance-level alignments, we cannot reintroduce it through a fragment alignment.


However, even though they are very large, they are still very incomplete, missing large fractions of possible relationships between common entities (West et al., 2014).

the task of inference over these knowledge bases, predicting new relationships simply by examining the knowledge base itself, has become increasingly important.


One major deficiency of random walk inference is the connectivity of the knowledge base graphif there is no path connecting two nodes in the graph, PRA cannot predict any relation instance between them.

prior work has introduced the use of a text corpus to increase the connectivity of the graph used as input to PRA (Lao et al., 2012;Gardner et al., 2013).


Contexts are extracted from syntactic dependencies generated by RASP (Briscoe et al., 2006), using nouns (heads of NPs) which have subject and direct object relations with the target verb.

each target verb is represented by a set of triples containing (i) the verb itself, (ii) a context noun and (iii) a syntactic relation (object, subject).


The neighbors of this verb are cause and play, which yield a good similarity score in WordNet.

although higher thresholds result in higher similarities for low and mid-frequency verbs, this comes at a cost, as the number of verbs included in the thesaurus decreases considerably.


In this setting we define a model that, given a predicate, identifies each of the semantic roles together with the syntactic path that links the predicate with the argument.

following previous work (Moschitti, 2004;Johansson, 2009), we take the syntactic path as the main source of syntactic features, but instead of just conditioning on it, we predict it together with the semantic role.


For English the distribution of path patterns is rather simple: the majority of paths consists of a number of ascending arcs followed by zero or one descending arc.

a common strategy in SRL systems, formulated by Xue and Palmer (2004), is to look for arguments in the ancestors of the predicate and their direct descendants.


our method runs this algorithm for each possible role of the predicate, obtaining the best paths to all arguments at each run.


Specifically, for each modifier token we only consider the dependencies or heads whose probability is above a factor γ of the most likely dependency for the given modifier.

γ = 1 selects only the most likely dependency (similar to a pipeline system, but without enforcing tree constraints), and as γ decreases more dependencies are considered, to the point where γ = 0 would select all possible dependencies.


The semantic information in terms of BCs or semantic similarity derived by LSA can have a major impact in case of data scarcity.

we trained our reranking models with increasing sizes of training data.


However, words in bold suggest that this conversation could be related to a transportation card.

we assume that a dialogue representation should be seen as a multi-view problem to substantiate the claims regarding the multi-theme representation of a given dialogue.


Indeed, an agent have to follow an predefined scenario of conversation.

the agent can find the main reason for the call which corresponds to the theme.


The general Joint Factor Analysis (JFA) paradigm (Kenny et al., 2008) considers multiple variabilities that may be cross-dependent.

jFA representation allows us to compensate the variability within sessions of a same speaker.


In opposition to a multinomial mixture model, LDA considers that a theme is associated to each occurrence of a word composing the document, rather than associate a topic to the complete document.

a document can change topics from a word to another one.


words) with each class composing the topic space.

a variability is added during the mapping process.


Thus, a set of p topic spaces are learned using LDA by varying the number of topics n to obtain p topic spaces of size n. The number of topics n varies from 10 to 3, 010.

a set of 3, 000 topic spaces is estimated.


The gap between the lower and the higher classification results is also important, with a difference of 25 points on the development set (the same trend is observed on the test set).

finding the best topic space size seems crucial for this classification task, particularly in the context of highly imperfect automatic dialogue transcriptions containing more than one theme.


The effectiveness of the proposed approach is evaluated in a classification task of theme dialogue identification.

the architecture of the system identifies conversation themes using the i-vector approach.


As mentioned earlier, the proposed model does not make any assumption about the feature space.

we examined whether the improvements it brings remain present even with a different feature space, for instance based on TF-IDF instead of BOW with counts.


The A-SC condition in all cases and the A+SC condition in some cases outperform the LSC baseline, which proves to be rather low, especially for the shell nouns with strict syntactic expectations (e.g., fact and reason).

we see that our algorithm is adding value.


The WSM model partitions nouns based on paradigmatic information (which sentence contexts a noun appears in), rather than SUN's use of syntagmatic information (which grammatical contexts a noun appears in).

it is perhaps not surprising that the noun classes derived by the WSM are organised thematically, and the synonym/co-hyponym structure observed in the SUN noun classes is in many cases absent (e.g., {Pferd (horse), Reiter (rider), Stall (stable), Sattel (saddle), Stute (mare)}; these classes can easily conflate semantic roles (e.g., Agent for rider and Location for stable), which is presumably unhelpful for representing selectional preferences.


For instance, the fact that three kittens have spots is immaterial to the solution.

aRIS has to determine what information is relevant to solving the problem.


For example in Figure 2 we know from the previous sentence that trees were in the park.

we assume that the unmentioned container is the park.


This is because ARIS learns to classify verb categories in sentences and does not require observing similar patterns/templates in the training data.

aRIS is more robust to differences between the training and test datasets and can generalize across different dataset types.


Since the C-value/NC-value metric based extraction method can obtain terms in strict linguistic patterns while the LLR measure based method ex-tracts more flexible terms, these two methods are complementary to each other.

we use these two methods to extract monolingual multiword terms and then combine the extracted terms.


However, in Figure 1-(b), "Wěiyuánhuì" and "Shěnyì" are translated separately.

"Wěiyuánhuì Shěnyì" is an unbracketable term in this sentence.


Since we only investigate the 1-best translations generated by the Combined-Model and single models, the translations generated by some single models (e.g., Brack-Model) may not be generated by the Combined-Model.

it is hard to say that the numbers of 1best translations affected by the Combined-Model must be greater than those of single models.


), and the channel model probability t( f i |e ) change.

the probability of a sample is simplified as shown Equation 6.


It is easy to prove that all other candidates that are not in the sorted list and with t( f i |e ) ≤ c have a upper bound probability: P(e i−1 e k e i+1 )•c.


Since the Malagasy monolingual data is collected from local websites, it is reasonable to argue that those data contain significant amount of information related to Africa.

we also collect 396 million tokens of African news in English from allAfrica.com.


Using lattice expansion rules, however, it is not necessary, as one lattice RHS can encode an arbitrary number of flat rules (see for example the RHS of X0 in figure 3).

we set the constraint that there is only one lattice expansion rule for each left-hand non-terminal.


Although it is a tree-to-tree system, we simplify the decoding step by "flattening" the target-side tree translation rules into string expansion rules (keeping track of the dependency structure in state features).

our setting is actually quite similar to that of many tree-to-string and string-to-string systems.


An example of this in Japanese-English translation is the fact that verbs in Japanese are "plural-independent", while the verb "to be" in English is not.

a RHS that is a candidate for translating a large part of a Japanese input sentence can easily use one of the variant of "to be" that is not consistent with the full sentence.


mized and makes use of advanced techniques with the language model (as the one described in (Heafield et al., 2013)) for which we do not have implemented an equivalent yet.

we are hopeful we can further improve our decoder in the future.


The top translation might sometimes be incorrect, or might lack some of the alternative representations that are very useful in retrieval.

considering the n highest scored translations (also referred to as the n-best list in MT literature) has become increasingly popular in CLIR approaches.


Labeling determines which query translation method is considered effective or not, which consequently determines what the "learning problem" is (since the objective of the classifier is to separate differently labeled instances).

there are two dimensions to consider when comparing labeling strategies.


/* Section 3.1.4 */ 7: Apply a document clustering algorithm on the mode-1 factor matrix to obtain the final clusters of documents unbalanced corpora.

no restriction is given on both the number L of languages and the distribution of documents over the languages ( Real-world documents often span multiple topics.


Upon the segment clustering, each document is represented by its segments assigned to possibly multiple segment clusters.

we derive a documentfeature matrix for each of the k segment clusters.


The dataset for supervised lexical substitution consists of sentences, containing an annotated target word t. Considering the sentence being the context for the target word, the target word might have different meanings.


4 http://www.nlm.nih.gov/bsd/licensee/ 2014_stats/baseline_med_filecount.html 5 https://code.google.com/p/jweb1t/ Besides the lexical substitution data sets for the open domain (McCarthy and Navigli, 2009;Biemann, 2012;Kremer et al., 2014) there is no dataset available that can be used for the medical domain.

we constructed an annotation task for the medical domain using a medical corpus and domain experts.


On average from the 10 terms 0.846 are annotated as correct substitutes.

the remaining terms can be used as false substitute candidates.


In order to represent the answer path differently to the surrounding subgraph (so the model can differentiate them), we double the dictionary size for entities, and use one embedding representation if they are in the path and another if they are in the subgraph.

we now learn a parameter matrix R k×N where N = N W + 2N S (N S is the total number of entities and relation types).


Unlike in constituency trees where all words reside at the leaf level, internal nodes of dependency trees are associated with words.

the dtrnn has to combine the current node's word vector with its children's hidden vectors to form h n .


However, simply training on the links in L may result in poor performance, because those links exhibit systematic differences from the mentions in M that the ranker will be applied to.

the reason is that our mention extractor attempts to populate M with all mentions, whereas L which contains only the specific subset of mentions that meet the hyperlinking conventions of Wikipedia, the features for M are distributed differently from those in L, and a model trained on L may not might not perform well on M. Our strategy is to leverage L to train an initial ranker, and then hand-label a small set of mentions from M to train a second-stage re-ranker that takes the ranking output of the initial ranker as a feature.


The RGM must estimate P(w j+1 |w 1: j , S 1:i ), however, since the first i lines have been encoded in the context vector u j i , we compute P(w j+1 |w 1: j , u j i ) instead.

the probability P(S i+1 |S 1:i ) becomes: Let |V | denote the size of the character vocabulary.


Fluency Coherence Meaning Poeticness Rank 5-char 7-char 5-char 7-char 5-char 7-char 5-char 7-char 5-char 7  the RNNPG estimates the probability of a poem line given at least one previous line.

the probability of a quatrain assigned by the RNNPG is the probability of the last three lines.


Given that we have a framework for generating general summaries, and the current popularity of the query-focused summarization task, we propose that the bulk of future work in this area be focused on the query-focused task specifically in assessing the relevance of a summary to a provided query.

we intend to pursue future research in utilizing word-sense disambiguation and synonyms, as well as other techniques for furthering REAPER's query similarity metrics in order to improve its ROUGE and human evaluation scores on query-focused tasks.


The score is scaled and ranges from 1 (bad) to 5 (good).

in table 3, the grammar score is the average score for each sentence and coherence score is the average for each topic.


In other words, a Deriv form is usually seen in the same surface form in a cluster of documents which are about the same topic.

the Deriv policy and the Raw policy achieve similar results for summarizing documents about the same topic.


This makes them applicable for sequential prediction tasks with arbitrary spatiotemporal dimensions.

their structure fits many NLP tasks, when the interpretation of a single sentence is viewed as analyzing a sequence of tokens.


However, this notion of depth likely does not involve hierarchical processing of the data: across different time steps, we repeatedly apply the same transformation to compute the memory contribution of the input (W ), to compute the response value from the current memory (U ) and to compute the next memory vector from the previous one (V ).

assuming the input vectors {x t } together lie in the same representation space, as do the output vectors {y t }, hidden representations {h t } lie in the same space as well.


H L , H R are respectively H's left dependents and right dependents, plus EOC (End-Of-Children), a special token to indicate that there are no more dependents to generate.

p (T (ROOT )) is the probability of generating the entire dependency structure T .


We use a neural network which consists of a weight matrix W 1 ∈ R n×n for left children and a weight matrix W 2 ∈ R n×n for right children to compute the vector for a parent node in a bottom up manner.

we compute p 1 as follows where b is a bias vector and f is an activation function (e.g., tanh or logistic).


For the three sampled features in Figure 6, the largest weights are dominated by: These features all seem very plausible, as observed in the experiments on indicator feature systems.

our model is able to automatically identify the most useful information for predictions, instead of hand-crafting them as indicator features.


Since we are exploiting automatically computed (and thus potentially noisy) stance information, we hypothesize that the effectiveness of such information would depend in part on the way it is exploited in RC systems.

we introduce a set of stance-supported models for RC, starting with simple pipeline models and then moving on to joint models with increasing sophistication.


Recent advances in automatic computer vision methods have started to make technologies for recognizing thousands of object categories a near reality (Perronnin et al., 2012;Deng et al., 2012;Deng et al., 2010;Krizhevsky et al., 2012).

there has been a spurt of recent work trying to estimate higher level semantics, including exciting efforts to automatically produce natural language descriptions of images and video (Farhadi et * Indicates equal author contribution.


Common challenges encountered in these pursuits include the fact that descriptions can be highly task dependent, openended, and difficult to evaluate automatically.

we look at the related, but more focused problem of referring expression generation (REG).


But, a person looking at a picture of one would probably call it a bird (unless they are very familiar with ornithology).

we include this attribute to capture how people name object categories in referring expressions.


material or shape attributes, but we lack sufficient data to train visual models for these infrequent attribute terms.

we model these attributes using only prior statisticsbased potentials ( §5.1.2).


A major challenge here is that many segments (like "Delphinus" in the above example) have multiple meanings.

wrong hypernyms may be adopted to generate incorrect candidate templates (like "CEO of [constellation]").


Specifically, we applied Hearst patterns (e.g., "NP [,] (such as | including) {N P, } * {and|or} NP") and isa patterns ("NP (is|are|was|were|being) (a|an|the) NP") to a large corpus containing 3 billion English web pages.

we obtained a term→hypernym bi-partite graph containing 40 million terms, 74 million hypernyms (i.e., category names), and 321 million edges (e.g., one example edge is "Berlin"→"city in Germany", where "Berlin" is a term and "city in Germany" is the corresponding hypernym).


Manually built semantic resources typically lack such kinds of scores.

it is very important to enhance them by deriving statistical data from a large corpus.


For instance, by comparing the P@20 scores of baseline-2 and NLP+C, we see a performance improvement of 14.3% (from 0.734 to 0.839).

every technique studied in this paper has its own merit in template generation.


(2012) and Kozareva and Hovy (2010) where every taxonomic relation is treated equally, we assume the confidence of each taxonomic relation is different, depending on the amount of Algorithm 1 Taxonomy Induction Algorithm Input: T : the taxonomic relation set Output: V : the vertex set of resultant taxonomy; E: the edge set of resultant taxonomy; 1: Initialize V = {ROOT }, E = ∅; 2: for each taxonomic relation (t 1 ≫ t 2 ) ∈ T do 3: if ∄ e(t 3 , t 1 ) ∈ E with t 3 ̸ = ROOT then 11: if ∃ e(ROOT, t 2 ) ∈ E then 14: end if 16: end for 17: edgeWeighting(V, E); 18: graphPruning(V, E); evidence it has.

the hypernym graph edges will be weighted as follows: Note that the Score value in formula (6) is determined by the taxonomic relation identification process described in Section 2.2.4.


For example, a trigram can be encoded as an MLN formula, For any given position (p), this formula has W 3 groundings, where W is the number of possible words, making it too large for learning/inference.

current MLN-based systems tend to include a highly simplified model ignoring powerful linguistic features.


A preliminary study of the BioNLP'13 training data suggests that 98.7% of the true triggers' head words 1 are either verbs, nouns or adjectives.

we consider only those words whose part-ofspeech tags belong to the above three categories as candidate triggers.


This is because most word tokens are one or two syllables, and only one of the two rhyme types of the weight grammar included a coda.

the HeavyRhyme symbol could simultaneously capture the most important aspects of both stress and coda constraints.


In contrast, nonparametric Bayesian language models aim to segment character string into a "reasonable" sentence according to the posterior probability.

theoretically, this method should have better ability to solve ambiguities over goodness measure based methods.


In this example, the word "狐 岐 山 (the Huqi Mountain)" in the unannotated sentence matches an item in the lexicon.

we obtain a partially-annotated sentence, in which the segmentation ambiguity of the characters "狐 (fox)", "岐 (brandy road)" and "山 (mountain)" are resolved ("狐" being the beginning, "岐" being the middle and "山" being the end of the same word).


This approach, however, cannot observe incorrect spacing since the assumption makes the training data devoid of negative example.

word spacers cannot use the spacing information given by the user, and erroneously alter the correctly spaced parts of the sentence.


It is difficult to automatically obtain the optimal for an arbitrary input sentence.

we set to 1, which, according to Figure 3, is more or less the optimal value for most of the test sets.


We can see that only 8 tags are presented in the annotated data for Danish, i.e, 4 tags (DET, PRT, PUNC, and NUM) are missing.

2 a classifier using all 12 tags will be heavily penalized in the evaluation.


The method we present here is more target-language oriented: our model is trained on the target language, in this way, only relevant information from the source language is retained.

we automatically correct the mapping, and other incompatibilities arising from incorrect alignments and syntactic divergence between the source and target languages.


It has been confirmed in many studies (Täckström et al., 2013, Das and Petrov, 2011, Toutanova and Johnson, 2008) that directly projected labels are noisy.


In the projective (arc-standard) case, subtrees that are in the buffer can be completely reduced.

each oracle step always combines an inferred entry in the table with either a node from the stack or a node from the reduced buffer, asymptotically reducing the time complexity.


The parsing times measured in our experiments for the static and the dynamic oracles are the same, since the oracle algorithm is only used during the training stage.

the reported improvements in parsing accuracy come at no extra cost for parsing time.


However, Chinese verbs can also modify nouns without morphological inflection, e.g., 養殖/farming 池/pond.

parsing Vt-N structures is difficult because it is hard to resolve such ambiguities without prior knowledge.


More specifically, in this paper, unknown words means words without semantic type information (i.e., E-HowNet expressions) and verbs without morphological structure information.

we propose a method for predicting the semantic types of unknown words, and use an affix database to train a morphstructure classifier to derive the morphological structure of verbs.


For example, the unknown word "傳 播 到 /disseminate", its prefix word is " 傳 播 /disseminate" and we learn that its semantic type is {disseminate|傳播} from E-HowNet.

we assign {disseminate|傳播} as the semantic type of " 傳 播 到 /disseminate".


The rationale is that, in general, high frequency Vt-N word-bigram is either VP or NP without ambiguity.

to obtain more accurate training data, we simply classify each high frequency Vt-N word bigram into a unique correct type without checking all of its instances.


(2011) seeks to solve NLP tasks without depending on the feature engineering of conventional NLP methods, our architecture is more practical because it integrates the neural networks into a well-tuned conventional method.

our tagger enjoys both the manually explored combinations of discrete features and the automatically learned non-linearity of the continuous features.


However, this causes error propagation problems, since each state depends on the history of the past decisions.

at the k-th iteration and the t-th position of the input sequence, we simply use the predictions of the previously learned classifiers to generate training examples, i.e., Although it is not theoretically justified, it empirically runs as a stochastic version of DAGGER (Ross et al., 2011) or SEARN (Daumé III et al., 2009) with the speed benefit of online learning.


Although it may be possible to compute the precision for each error type in the pipeline system (since we know which correction was proposed by which classifier), this is more difficult to do in the SMT and combined system, where we would need to rely on heuristics which are more prone to errors.

we decided to analyze a sample of 200 sentences by hand for a comparatively more robust comparison.


In the annotated data, 1102 sentences are in a single matrix (398 Hi, 698 En and 6 Ot) and in 45 posts there is at least one switch of matrix (mostly between Hi and En.


On the other hand, only 7.34% En-matrix comments feature CM (again almost always with Hi).

a total of 17.2% comments/posts, which contains a quarter of all the words in the annotated corpus, feature either CM or code-switching or both.


(2013) illustrated, even extremely coarse features derived from SALT annotations, for example a binary feature for each utterance indicating the presence of any error codes, can be of immense utility for identifying language impairments.

we will evaluate our system as a binary tagger: each utterance, both in the manually annotated data and system output either contains an error code, or it does not.


Furthermore, our approach has a critical difference from manual annotation: we perform classification one utterance at a time, while manual annotators have access to the context of an utterance.

certain types of errors, for example using a pronoun of the wrong gender, or responding ungrammatically to a question (ex.


However, our model will assign all of these an equal score, as they use the same sequence of lexical categories.

we extend our model with a simple deterministic heuristic for ranking parses that use the same lexical categories.


A major motivation for CCG parsing is to exploit its transparent interface to the semantics, allowing syntactic parsers to do much of the work of semantic parsers.

perhaps the most relevant measure of the performance of a CCG parser is its effect on the accuracy of downstream applications.


30 annotations were deemed to have "no obvious errors," 15 only minor errors, 3 a major error (i.e., clear violation of annotation guidelines), 4 4 a major error and at least one minor error, and 8 as containing multiple major errors.

75% are judged as having no major errors.


For example, the constraints that force z to encode a valid tree can be exploited within the framework by making calls to classic arborescence algorithms (Chu and Liu, 1965;Edmonds, 1967).

when describing modifications to TurboParser, we need only to explain additional constraints and features imposed on parts.


1 Dependency parsing is typically guided by parameterized scoring functions that involve rich features exerting refined control over the choice of parse trees.

finding the highest scoring parse tree is a provably hard combinatorial inference problem (McDonald and Pereira, 2006).


samples from any first-order distributions.

we could initialize the tree y (0) with a tree from a first-order parser, or draw the initial tree from a first-order distribution other than uniform.


Since the average sentence length is 24, the discrepancy between the typical number (e.g., 121) and the worst case (2 24−1 ) is substantial.

only a small number of restarts is likely to suffice for finding optimal trees in practice.


For sentence S, there are ∏ n i=1 |Sense W N (w i )| possible mapping answers, which are impractical to compute.

we design two simple algorithms, L2R (left to right) algorithm and S2C (simple to complex) algorithm, for word sense disambiguation based on the sense vectors.


If the score margin between the maximum and the second maximum is larger than the threshold ε, we are confident with the disambiguation result of w i and then use the sense vector to replace the word vector in the context vector.

we obtain a more accurate context vector for other words that are still yet to be disambiguated.


The resulting S-vectors are concatenated, after the subject and object nouns have been combined with their matrices, and combined with the softmax component to produce the output distribution.

the objective function is the same as in Equation 1, but h V is defined as: where || represents vector concatenation.


Both Romanian and Turkish borrowed words from French massively towards the end of the 19 th century.

most pairs of Romanian-Turkish cognates have common French ancestors, and words in Romanian and Turkish which resemble are actually loans from the same French words.


Greek is the only language which reaches its peak regarding the similarity with Romanian in the 19 th century (due to the brief Phanariot dominance in the 19 th century).

romanian preserved its Latin character all along, and the influence of the non-Latin languages on romanian (overestimated in some works) was in fact not so significant.


Also, deeper colors around diagonal line in (a) (c) indicate a more balanced combination of refiners and urls are more likely to enhance intent understanding.

we conclude clicks has a weak advantage over refiners in improving the result, while combining both with proper parameters can get the best result.


In practice, relevant properties may be difficult to distinguish from uninteresting statements in Web documents.

assertions extracted from Web documents include the facts that companies may "say in a statement", or that hurricanes may "be just around the corner" or may "be in effect".


Queries as Knowledge: Users tend to formulate their Web search queries based on knowledge that they already possess at the time of the search (Paşca, 2007).

search queries play two roles simultaneously: in addition to requesting new information, they indirectly convey knowledge in the process.


Moreover, these linked data usually have complex structures and are highly heterogeneous.

there are gaps for users regarding access.


Although a few experts can write queries using structured languages (such as SPARQL) based on their needs, this skill cannot be easily utilized by common users (Christina and Freitas, 2014).

providing user-friendly, simple interfaces to access these linked data becomes increasingly more urgent.


If we know that the phrase California can refer to the entity California State, and which can be the second argument of the relation foundationPlace, together with a verb phrase being more likely to be mapped to Relation, we should map the phrase founded in to foundationPlace in this question.

we aim to determine if joint disambiguation is better than individual disambiguation.


Furthermore, these specially designed patterns may not be valid with variations in domains or languages.

another important question arises: can we automatically learn rules or patterns to achieve the same ob-jective?


Each step can provide useful information for the other two tasks.

performing joint inference can effectively improve the performance.


1) Our method is a datadriven approach that can learn effective patterns or rules for the task.

it exhibits more robustness and adaptability for various KBs.


Telegraphic utterances will generally be even more challenging.

whereas TREC-QA/NLP-style research has focused on parsing and precise interpretation of a well-formed query sentence to a strongly structured (typically graph-oriented) query language (Kasneci et al., 2008;Pound et al., 2012;Yahya et al., 2012;Berant et al., 2013;Kwiatkowski et al., 2013), the Web search and information retrieval (IR) community has focused on telegraphic queries (Guo et al., 2009;Sarkas et al., 2010;Li et al., 2011;Sawant and Chakrabarti, 2013).


The accuracy of any specific segmentation is expected to be low in such adversarial settings.

support for an answer entity is aggregated over several segmentations.


E.g., the (directed) relation type /location/country/capital connects from from /location/country to /location/citytown.

"capital" can be added to the set of descriptive phrases of entity type /location/citytown.


Our corpus is not directly annotated with relation mentions.

we get from relations to documents via high-confidence phrases.


Volunteers were asked to find answers using single Freebase pages.

by construction, queries retained can be answered using the Freebase KG alone, with a simple r(e 1 , ?)


However, relying solely on corpus annotations fails to retrieve many potential evidence snippets, because entity annotations are sparse.

we probed the token index with the textual mention of e 1 in the query; this improved recall.


However, in vote prediction, author constraints are not useful because a user is not permitted to cast more than one vote per question, unlike in stance prediction, where users may engage in a debate and therefore post more than once per debate topic.

we propose two new types of constraints for exploiting inter topic user voting patterns.


One of the major inherent difficulties of our task is that it consists not of one, but of 4,803 separate multiclass classification problems (one for each question).

our approach to the problem necessarily has to be somewhat generic, as it would be too time-consuming to develop an appropriate feature set for each question.


To form an emotion dependency feature, we take a regular dependency feature and replace each of its words where possible with the emotion it evokes as determined by the NRC Emotion Word Lexicon.

from the dependency:(nsubj,inept,obama) example above, we would generate three features: emotiondependency:(nsubj,anger,obama), emotiondependency:(nsubj,disgust,obama), and emotiondependency:(nsubj,negative,obama).


Because some of the feature types (n-grams, cue words, dependencies, and emotion dependencies) described in the previous subsection are expected to generate a large number of non-predictive features, we trim some of the most irrelevant features out of the feature set to avoid memory problems.

following Yang and Pedersen (1997), for each question we calculate the information gain of each feature of these types on the training set.


To most of the application developers, the parser is more like a blackbox, which is not directly programmable.

it is non-trivial to adapt these generic parsers to language-specific social media text.


2) Community emotion aggregation: We assume that in a given community, different users have different weights when aggregating community emotion.

we propose a novel weighting approach based on the user's authority.


It is obvious that, for all commu- nities, HITS-based weighting approach performs better than equal weighting approach.

we can conclude that user authority is important when aggregating community emotion.


Unlike the publicly available ARGUE corpus based on the online debate forum 4forums.com (Abbott et al., 2011), our corpus is based on Slashdot, which is a general purpose forum not targeted to debates.

we expect that detecting disagreement may be a more difficult task in our new corpus, as certain topics (like discussing GPS systems) may be targeted towards objective information sharing without any participants expressing opinions or stances.


Furthermore, we hypothesize that individual discourse markers might have low frequency counts in the text.

we also include an aggregated count of all 17 discourse markers in each fifth of the posts in a topic (e.g.


We have discovered that there is significant sense ambiguity, meaning that words often have mixtures of senses among the classes +effect, -effect, and Null.

we develop a sense-level rather than word-level lexicon.


One of our goals is to investigate whether the +/-effect property tends to be shared among semantically-related senses, and another is to use a method that applies to all word senses, not just to the senses of words in a given word-level lexicon.

we build a graph-based model in which each node is a WordNet sense, and edges represent semantic WordNet relations between senses.


Sometimes, events have positive or negative effects on agents or other entities as well.

in this paper, we consider a sense to be +effect (-effect) if it has +effect (-effect) on an entity, which may be the agent, the theme, or some other entity.


Mappings between FrameNet and WordNet are not perfect.

we opted to manually annotate the senses of the words in the word-level lexicon.


LGC fits in a univariate regularization framework, where the output matrix is treated as the only variable in optimization, and the optimal solutions can be easily obtained by solving a linear system.


Through this experiment, we see that since each type of information has a different character, we need different models to maximize the effectiveness of each type.

the hybrid method with different models can have better performance.


The basic idea of our method is that the translation similarity of two pages can be inferred from their neighbor pages, which can be adopted as an important source of external similarity.

the translation similarity of page pairs will influence each other.


Some existing parallel corpora are subject to subscription or license fee and thus not freely available, while others are domain-specific.

a lot of previous research has focused on automatically mining parallel corpora from the web.


Another parameter can be easily adjusted to achieve the best performance.

our method can be used in other websites with different styles, without much effort to optimize these parameters.


For each web page, our method views link-in and link-out hyperlinks as the same.

the linked pages will influence each other in estimating the translation similarity.


However, our method is language-independent.

it can be applied to other language pairs by only replacing a bilingual lexicon.


According to Equation (1), ( ) depends on ( ) and ( ) .


( ) will converge after a certain number of iterations.

( ) is defined as the enhanced similarity of page and after the i-th iteration, and the same is for ( ) and ( ( ) ( )) .


In addition, their content part is very short and contains the same or related topics.

the links in other 4 parts are likely to be similar.


Typing the prefix of this string will update the autocomplete dropdown with matching strings from the gray text.

sometimes the autocomplete dropdown will contain suggestions from several positions in the full suggested translation.


13 Furthermore, this "freezing" of bi-phrases can be repeated after each iteration of rewriter.

we now have an oracle situation for choosing which source phrases may be rewritten, but the rest of the rewriting procedure is still fully automatic.


The smaller improvement in the former situation may be attributed to the nature of the target language which has a simpler agreement system.

the form-related errors discussed in Section 4.4 are possibly less subject to improvement here.


Therefore, similar to prior work, we gradually expand the lexicon during learning.

the parameter space Algorithm 1 Batch algorithm for maximizing L (θ, Λ, D).


This time, however, we do not use a sentence-specific lexicon, but instead use the model lexicon that has been expanded with all voted entries.

the set of max-scoring parses producing the correct execution may be different compared to GENENTRIES.


We defined this alternative formalization in order to make LSCs compatible with Neo-Davidsonian, event-based, semantic theories.

this form of LSC formalization is wellsuited for representing the semantics of natural language utterances.


Our semantic LSC objects have the form of a tree (reflecting the sequence of nested events in our scenarios).

we can use standard tree evaluation metrics, such as ParseEval (Black et al., 1992), to evaluate the accuracy of the prediction.


Though the true underlying word distribution for each document is unknown and could be far from the sample observationM , the empirical word-word matrixQ converges to its expectation AE[W W T ]A T = ARA T as the number of documents increases.

the learning task is to approximately recover A and R pretending that the empiricalQ is close to the true second-order moment matrix Q.


Unlike (Arora et al., 2013), which presents results on synthetic datasets to compare performance across different recovery methods given increasing numbers of documents, we are are interested in comparing anchor finding methods, and are mainly concerned with semantic quality.

although we have conducted experiments on synthetic document collections, 5 we focus on real datasets for this work.


The most frequent terms in the overall corpus distribution p(w) often appear together in documents.

a model creating many topics similar to the corpus distribution is likely to achieve high Coherence, but low Specificity by definition.


Note that we are only varying the anchor selection part of the Anchor Words algorithm in these experiments, recovering topic-word distributions in the same manner given anchor words.

any differences between topics with the same anchor word (for example chicken) are due to the difference in either the number of topics or the rest of anchor words.


No labels were used except for final evaluation on the test data.

we need a way to handle unknown words in test data.


The focus of this work is to show that given an effective batch translation system and predictions, we can learn a policy that will turn this into a simultaneous translation system.

to separate translation errors from policy errors, we perform experiments with a nearly optimal translation system we call an omniscient translator.


In the case where y j is unaligned, the closest aligned target word to y j that has a corresponding alignment entry is aligned to x i ; then, if x i is present in the input, y j appears in the output.

our omniscient translation system will always produce the correct output given the correct input.


Theseỹ j are sampled from the ibm Model 1 lexical probability table multiplied by the source language modelỹ j ∼ Mult(θx i )p LM (x).

8 even if we predict the correct verb using a next word action, it will be in the wrong position and thus generate a translation from the lexical probabilities.


Moreover, the trend is towards unsupervised (rather than supervised) learning methods due to the lack in most languages of annotated data and the applicability in wider domains (Merlo et al., 2010).

despite its difficulty, unsupervised PCFG grammar induction (or induction of other similarly expressive models) is still an important task in computational linguistics.


On the downside, one bad merge can have a disastrous ripple effect.

to minimize as much as possible the chance of this happening, every merge undertaken is the best possible one at that point in time (w.r.t.


We show that all the possible SC-CFGs in CNF consistent with the congruence classes directly correspond to all the solutions of a boolean formula built upon the congruence classes, where the variables of this formula correspond to nonterminals (and, with some minor adjustments, production rules as well).

finding the smallest possible grammar directly translates into finding a solution which has the smallest possible amount of true variables.


The p-spectrum kernel assigns a high score even when the p-gram has a high frequency in only one of the two strings.

the intersection kernel captures something about the correlation between the p-gram frequencies in the two strings, which may lead to a more sensitive similarity between strings.


However, the corpus was originally collected for the purpose of corpus linguistic investigations, and because of this contains some idiosyncrasies that make it problematic for the task of NLI (Brooke and Hirst, 2012).

a modified version of the corpus that has been normalized as much as possible for topic and character encoding (Tetreault et al., 2012) is used.


Other authors (Bykh and Meurers, 2012;Popescu and Ionescu, 2013) also report better results by using n-grams with the length in a range, rather than using n-grams of fixed length.

the results reported in this work are based on blended string kernels based on 5-8 n-grams.


Here, interactive approaches use human labels during the learning process; they present a node for humans to label, and based on this label, the algorithms compute the next node to be presented to the humans.

in interactive algorithms, human labeling and computations of the next node must run concurrently.


We call the set of nodes in a graph from which samples will be taken in the next round a seed pool.

in Figure 2, the set of nodes with red rectangles is the seed pool.


The merged graph is expected to inherit the characteristics of its original graphs.

applying graph-based active learning to the merged graph is expected to sample nodes in accordance with the characteristics of its original graphs.